Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Visual System01:26

Visual System

1.4K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
1.4K
Visual Agnosia01:12

Visual Agnosia

689
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
689
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.5K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.5K
Gestalt Principles of Perception01:21

Gestalt Principles of Perception

854
Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
854
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.9K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.9K
Sensation01:21

Sensation

1.1K
Sensory receptors are specialized neurons that respond to specific types of external stimuli, initiating the process known as sensation. This occurs when sensory input, such as light entering the eye, is detected by these receptors, causing chemical changes in the cells of the retina. These cells then convert the sensory stimulus into action potentials that are transmitted to the central nervous system, a process termed transduction.
Absolute thresholds can quantify the sensitivity of sensory...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Large Language Models Estimate Fine-Grained Human Color-Concept Associations.

Cognitive science·2026
Same author

Understanding the opaque-is-more bias and saturated-is-more bias for colormap data visualizations.

Attention, perception & psychophysics·2026
Same author

Affective Color Scales for Colormap Data Visualizations.

IEEE transactions on visualization and computer graphics·2025
Same author

Perceptual and Cognitive Foundations of Information Visualization.

Annual review of vision science·2025
Same author

More of what? Dissociating effects of conceptual and numeric mappings on interpreting colormap data visualizations.

Cognitive research: principles and implications·2023
Same author

Unifying Effects of Direct and Relational Associations for Visual Communication.

IEEE transactions on visualization and computer graphics·2022
Same journal

LivingAvatars: Robust Head Reconstruction With Gaussian Lifecycle Management and Neural Detail Synthesis.

IEEE transactions on visualization and computer graphics·2026
Same journal

Two-phase Impulse Fluid on Particle Flow Map.

IEEE transactions on visualization and computer graphics·2026
Same journal

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Dec 3, 2025

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

2.7K

Semantic Discriminability for Visual Communication.

Karen B Schloss, Zachary Leggon, Laurent Lessard

    IEEE Transactions on Visualization and Computer Graphics
    |October 26, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Semantic discriminability, not just color difference, improves understanding of data visualizations. Clearer concept-to-color mapping enhances interpretation, independent of perceptual distance.

    More Related Videos

    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    9.4K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.8K

    Related Experiment Videos

    Last Updated: Dec 3, 2025

    Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
    05:38

    Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

    Published on: June 29, 2021

    2.7K
    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    9.4K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.8K

    Area of Science:

    • Cognitive Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Interpreting information visualizations requires mapping visual features to concepts.
    • This mapping relies on both perceptual discriminability (distinguishing visual features) and semantic discriminability (inferring unique concept-feature mappings).
    • Previous studies suggested semantic discriminability aids interpretation, but perceptual distance confounds results.

    Purpose of the Study:

    • To independently assess the effects of semantic distance and perceptual distance on semantic discriminability in bar graph visualizations.
    • To determine which factor, semantic or perceptual distance, primarily influences performance in interpreting data visualizations.

    Main Methods:

    • Conducted two experiments using bar graph data visualizations.
    • Manipulated semantic distance (concept-feature association) and perceptual distance (visual feature difference) independently.
    • Ensured perceptual distance was sufficient for noticeable color differences.

    Main Results:

    • Increasing semantic distance significantly improved performance in interpreting visualizations.
    • This improvement was observed independently of variations in perceptual distance.
    • When semantic and perceptual distances were uncorrelated, performance was primarily driven by semantic distance.

    Conclusions:

    • Semantic discriminability is a crucial factor for effective interpretation of information visualizations, beyond mere perceptual distinctness.
    • Optimizing color palettes for data visualization should prioritize semantic discriminability to enhance user understanding.
    • Findings inform design choices in visual communication, particularly regarding color mapping strategies.